"""simple docstring"""



from typing import Dict, Optional

import numpy as np

import datasets


__lowercase		:		Any								=				'''
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,
the mean IoU of the image is calculated by taking the IoU of each class and averaging them.
'''

__lowercase		:		Any								=				'''
Args:
    predictions (`List[ndarray]`):
        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    references (`List[ndarray]`):
        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    num_labels (`int`):
        Number of classes (categories).
    ignore_index (`int`):
        Index that will be ignored during evaluation.
    nan_to_num (`int`, *optional*):
        If specified, NaN values will be replaced by the number defined by the user.
    label_map (`dict`, *optional*):
        If specified, dictionary mapping old label indices to new label indices.
    reduce_labels (`bool`, *optional*, defaults to `False`):
        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,
        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.

Returns:
    `Dict[str, float | ndarray]` comprising various elements:
    - *mean_iou* (`float`):
        Mean Intersection-over-Union (IoU averaged over all categories).
    - *mean_accuracy* (`float`):
        Mean accuracy (averaged over all categories).
    - *overall_accuracy* (`float`):
        Overall accuracy on all images.
    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):
        Per category accuracy.
    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):
        Per category IoU.

Examples:

    >>> import numpy as np

    >>> mean_iou = datasets.load_metric("mean_iou")

    >>> # suppose one has 3 different segmentation maps predicted
    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])
    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])

    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])
    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])

    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])
    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])

    >>> predicted = [predicted_1, predicted_2, predicted_3]
    >>> ground_truth = [actual_1, actual_2, actual_3]

    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), \'per_category_accuracy\': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}
'''

__lowercase		:		Union[str, Any]								=				'''\
@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,
author = {{MMSegmentation Contributors}},
license = {Apache-2.0},
month = {7},
title = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},
url = {https://github.com/open-mmlab/mmsegmentation},
year = {2020}
}'''





def  SCREAMING_SNAKE_CASE							(  snake_case,   snake_case,   snake_case,   snake_case,   snake_case = None,   snake_case = False,   ):

     if label_map is not None:
          for old_id, new_id in label_map.items():
               __snake_case				=				new_id

    # turn into Numpy arrays
     __snake_case				=				np.array(_A)
     __snake_case				=				np.array(_A)

     if reduce_labels:
          __snake_case				=				2_55
          __snake_case				=				label - 1
          __snake_case				=				2_55

     __snake_case				=				label != ignore_index
     __snake_case				=				np.not_equal(_A,   _A)
     __snake_case				=				pred_label[mask]
     __snake_case				=				np.array(_A)[mask]

     __snake_case				=				pred_label[pred_label == label]

     __snake_case				=				np.histogram(_A,   bins=_A,   range=(0, num_labels - 1))[0]
     __snake_case				=				np.histogram(_A,   bins=_A,   range=(0, num_labels - 1))[0]
     __snake_case				=				np.histogram(_A,   bins=_A,   range=(0, num_labels - 1))[0]

     __snake_case				=				area_pred_label + area_label - area_intersect

     return area_intersect, area_union, area_pred_label, area_label





def  SCREAMING_SNAKE_CASE							(  snake_case,   snake_case,   snake_case,   snake_case,   snake_case = None,   snake_case = False,   ):
     __snake_case				=				np.zeros((num_labels,),   dtype=np.floataa)
     __snake_case				=				np.zeros((num_labels,),   dtype=np.floataa)
     __snake_case				=				np.zeros((num_labels,),   dtype=np.floataa)
     __snake_case				=				np.zeros((num_labels,),   dtype=np.floataa)
     for result, gt_seg_map in zip(_A,   _A):
          __snake_case		,						__snake_case		,						__snake_case		,						__snake_case				=				intersect_and_union(
              _A,   _A,   _A,   _A,   _A,   _A)
          total_area_intersect += area_intersect
          total_area_union += area_union
          total_area_pred_label += area_pred_label
          total_area_label += area_label
     return total_area_intersect, total_area_union, total_area_pred_label, total_area_label





def  SCREAMING_SNAKE_CASE							(  snake_case,   snake_case,   snake_case,   snake_case,   snake_case = None,   snake_case = None,   snake_case = False,   ):
     __snake_case		,						__snake_case		,						__snake_case		,						__snake_case				=				total_intersect_and_union(
         _A,   _A,   _A,   _A,   _A,   _A)

     # compute metrics
     __snake_case				=				{}

     __snake_case				=				total_area_intersect.sum() / total_area_label.sum()
     __snake_case				=				total_area_intersect / total_area_union
     __snake_case				=				total_area_intersect / total_area_label

     __snake_case				=				np.nanmean(_A)
     __snake_case				=				np.nanmean(_A)
     __snake_case				=				all_acc
     __snake_case				=				iou
     __snake_case				=				acc

     if nan_to_num is not None:
          __snake_case				=				{metric: np.nan_to_num(_A,   nan=_A) for metric, metric_value in metrics.items()}

     return metrics




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,			_KWARGS_DESCRIPTION       )
class   _A						(    datasets.Metric       ):




    """simple docstring"""




    def 			lowercase       (		self				:  str     )     ->							List[Any]:
         return datasets.MetricInfo(
             description=_DESCRIPTION						,   citation=_CITATION						,   inputs_description=_KWARGS_DESCRIPTION						,   features=datasets.Features(
                 # 1st Seq - height dim, 2nd - width dim
                 {
                     '''predictions''': datasets.Sequence(datasets.Sequence(datasets.Value('''uint16'''     )     )     ),
                     '''references''': datasets.Sequence(datasets.Sequence(datasets.Value('''uint16'''     )     )     ),
                 }     )						,   reference_urls=[
                 '''https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py'''
             ]						,   )







    def 			lowercase       (		self				:  Tuple						,   A_				:  Optional[Any]						,   A_				:  Optional[int]						,   A_				:  int						,   A_				:  bool						,   A_				:  Optional[int] = None						,   A_				:  Optional[Dict[int, int]] = None						,   A_				:  bool = False						,   )     ->							Any:
         __snake_case				=				mean_iou(
             results=__UpperCamelCase						,   gt_seg_maps=__UpperCamelCase						,   num_labels=__UpperCamelCase						,   ignore_index=__UpperCamelCase						,   nan_to_num=__UpperCamelCase						,   label_map=__UpperCamelCase						,   reduce_labels=__UpperCamelCase						,   )
         return iou_result

"""simple docstring"""



import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


a       :						int       		=		5_0_0_0_0_0

a		,			a       :						Union[str, Any]       		=		os.path.split(__file__)
a       :						Dict       		=		os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))


@get_duration
def 			_UpperCamelCase						(    _A       ,   **_A   )     ->	Any:





    """simple docstring"""

    _UpperCAmelCase        = dataset.map(**_A   )


@get_duration
def 			_UpperCamelCase						(    _A       ,   **_A   )     ->	Dict:





    """simple docstring"""

    _UpperCAmelCase        = dataset.filter(**_A   )


def 			_UpperCamelCase						(    )     ->	Tuple:





    """simple docstring"""

    _UpperCAmelCase        = {"""num examples""": SPEED_TEST_N_EXAMPLES}
    with tempfile.TemporaryDirectory() as tmp_dir:
        _UpperCAmelCase        = datasets.Features({"""text""": datasets.Value("""string"""   ), """numbers""": datasets.Value("""float32"""   )}   )
        _UpperCAmelCase        = generate_example_dataset(
            os.path.join(_A       ,   """dataset.arrow"""   )       ,   _A       ,   num_examples=_A   )

        _UpperCAmelCase        = transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""       ,   use_fast=_A   )

        def tokenize(_A   ):
            return tokenizer(examples["""text"""]   )

        _UpperCAmelCase        = map(_A   )

        _UpperCAmelCase        = map(_A       ,   batched=_A   )

        _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""numpy"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""pandas"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""torch"""       ,   columns="""numbers"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""tensorflow"""       ,   columns="""numbers"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        _UpperCAmelCase        = map(_A       ,   function=_A       ,   batched=_A   )

        _UpperCAmelCase        = filter(_A   )

        # Activate later when tokenizer support batched inputs
        # with dataset.formatted_as(type='numpy'):
        #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

    with open(_A       ,   """wb"""   ) as f:
        f.write(json.dumps(_A   ).encode("""utf-8"""   )   )


if __name__ == "__main__":  # useful to run the profiler
 benchmark_map_filter()